2009 MatrixFactorizationTechniquesfo

• (Koren et al., 2009) ⇒ Yehuda Koren, Robert Bell, and Chris Volinsky. (2009). “Matrix Factorization Techniques for Recommender Systems.” In: Computer Journal, 42(8). doi:10.1109/MC.2009.263

Subject Headings: Matrix Factorization-based Item Relevance Ranking Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%222009%22+Matrix+Factorization+Techniques+for+Recommender+Systems
• http://dl.acm.org/citation.cfm?id=1608565.1608614&preflayout=flat#citedby

Quotes

Abstract

As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest-neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.

References

• 1. D. Goldberg, et al., "Using Collaborative Filtering to Weave an Information Tapestry," Comm. ACM, vol. 35, 1992, pp. 61-70.
• 2. B.M. Sarwar, et al., "Application of Dimensionality Reduction in Recommender System—A Case Study," Proc. KDD Workshop on Web Mining for e-Commerce: Challenges and Opportunities (WebKDD), ACM Press, 2000.
• 3. S. Funk, "Netflix Update: Try This at Home," Dec. 2006; http://sifter.org/~simon/journal/20061211.html.
• 4. Y. Koren, "Factorization Meets the Neighborhood: A Multifaceted Collaborative Filtering Model," Proc. 14th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining, ACM Press, 2008, pp. 426-434.
• 5. A. Paterek, "Improving Regularized Singular Value Decomposition for Collaborative Filtering," Proc. KDD Cup and Workshop, ACM Press, 2007, pp. 39-42.
• 6. G. Takàcs, et al., "Major Components of the Gravity Recommendation System," SIGKDD Explorations, vol. 9, 2007, pp. 80-84.
• 7. R. Salakhutdinov, and A. Mnih, "Probabilistic Matrix Factorization," Proc. Advances in Neural Information Processing Systems 20 (NIPS 07), ACM Press, 2008, pp. 1257-1264.
• 8. R. Bell, and Y. Koren, "Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights," Proc. IEEE Int'l Conf. Data Mining (ICDM 07), IEEE CS Press, 2007, pp. 43-52.
• 9. Y. Zhou, et al., "Large-Scale Parallel Collaborative Filtering for the Netflix Prize," Proc. 4th Int'l Conf. Algorithmic Aspects in Information and Management, LNCS 5034, Springer, 2008, pp. 337-348.
• 10. Y.F. Hu, Y. Koren, and C. Volinsky, "Collaborative Filtering for Implicit Feedback Datasets," Proc. IEEE Int'l Conf. Data Mining (ICDM 08), IEEE CS Press, 2008, pp. 263-272.
• 11. Y. Koren, "Collaborative Filtering with Temporal Dynamics," Proc. 15th ACM SIGKDD Int'l Conf. Knowledge Discovery and Data Mining (KDD 09), ACM Press, 2009, pp. 447-455.
• 12. J. Bennet, and S. Lanning, "The Netflix Prize," KDD Cup and Workshop, 2007; www.netflixprize.com.

BibTeX

@article{2009_MatrixFactorizationTechniquesfo,
  author    = {Yehuda Koren and
               Robert M. Bell and
               Chris Volinsky},
  title     = {Matrix Factorization Techniques for Recommender Systems},
  journal   = {Computer},
  volume    = {42},
  number    = {8},
  pages     = {30--37},
  year      = {2009},
  url       = {https://doi.org/10.1109/MC.2009.263},
  doi       = {10.1109/MC.2009.263},
}

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 MatrixFactorizationTechniquesfo	Yehuda Koren Chris Volinsky Robert Bell			Matrix Factorization Techniques for Recommender Systems				10.1109/MC.2009.263		2009